Efficacy of talc-based formulation of Beauveria bassiana (Bals.) Vuill. (MZ749636) against two spotted spider mite, Tetranychus urticae Koch

Tetranychus urticae is one of the most important and destructive mite species infesting the vegetable crops, which is very important to control because of its wide host range. The damage caused by the two spotted spider mite can be eliminated by using entomopathogenic fungi, as the acaricidal control causes various problems such as resistance, resurgence and residue problems. Therefore, the present study was carried out with a view to evaluate the efficacy of the talc-based formulation of Beauveria bassiana (MZ749636) under pot-culture conditions against two spotted spider mite (TSSM), T. urticae in potted bhendi (Abelmoschus esculentus) plants, in comparison with fenazaquin 10 EC @ 1.5ml/l, azadirachtin 3000 ppm @ 2ml/l and crude formulation of B. bassiana, MZ749636 @ 1 x 108 conidia/ml. After two rounds of spraying at fortnight intervals, the talc- formulation of B. bassiana, MZ749636 resulted in 62.83 per cent cumulative reduction of TSSM over the control. However, fenazaquin 10 EC @1.5 ml/l recorded the maximum cumulative mortality of 80.07 per cent, followed by azadirachtin 3000 ppm @ 2ml/l, which recorded a cumulative mortality per cent of 71.06 per cent. Crude formulation of B. bassiana recorded 58.12 per cent reduction of TSSM over the control after two rounds of spraying. This was the first study that attempted to evaluate the efficacy of the talc formulation of the B. bassiana (MZ749636) isolate against TSSM

Degree days and demography of Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae) on maize at different temperatures

The temperature has a direct effect on the activity of insect pests and their developmental rate. The increasing temperature could profoundly influence the population dynamics, life cycle, length of reproduction, fecundity, and longevity. In the present study, the impact of different temperatures (32, 33, 34, 35 and 36°C) on the degree days and population fitness of Spodoptera frugiperda (J. E. Smith) was evaluated under artificial conditions. The results showed that for S. frugiperda, an average of 690.38 degree days was required to complete the total life span. The total larval developmental time, pupal duration and adult longevity required 237.38, 184.47 and 228.10 degree days, respectively. The life history data of S. frugiperda were analysed by using TWOSEX-MSChart. An increase in temperature reduced the developmental time of S. frugiperda at age x and stage j. The highest reproductive value (vx) of S. frugiperda was obtained at 34°C (600 individuals per day) and was found to be reduced at a further increase in temperature of 35°C (260 individuals per day) and 36°C (120 individuals per day). These results signify the improved fitness of S. frugiperda with increasing temperature levels, and the degree days help to predict the development pattern of S. frugiperda based on heat accumulation

Android application development for identifying maize infested with fall armyworms with Tamil Nadu Agricultural University Integrated proposed pest management (TNAU IPM) capsules

Several pests and diseases wreak havoc on maize crops worldwide. Novel and rapid methods for detecting pests and diseases in real-time will make monitoring them and designing effective management measures easier. In the recent past, maize has been imperilled by fall armyworms (Spodoptera frugiperda), which have caused substantial yield losses in maize. This study aimed to create an Android mobile application via  DCNN (Deep Convolutional Neural Network)-based AI pest detection system for maize producers. Everyone benefits from the deployment of these CNN models on mobile phones, especially farmers and agricultural extension professionals because it makes them more accessible. Automatic diagnosis of plant pest infestations from captured images through computer vision and artificial intelligence research is feasible for technological advancements. Therefore, early detection of maize fall armyworm (FAW) infestation and providing relevant recommendations in maize could result in intensified maize crop yields. . An Android mobile application was created to identify fall armyworm infection in maize and included the recommendations given by Tamil Nadu Agricultural University proposed Integrated Pest Management (TNAU IPM ) capsules in the mobile app on as to how to deal with such a problem. Digital and novel technology was chosen to address these issues in maize. Deep convolutional neural networks (DCNNs) and transfer learning have recently moved into the realm of just-in-time crop pest infestation detection, following their successful use in a variety of fields. The algorithm accurately detects FAW (S. frugiperda) infected areas on maize with 98.47% training accuracy and 93.47% validation accuracy

Artificial intelligence-powered expert system model for identifying fall armyworm infestation in maize (Zea mays L.)

Maize (Zea mays L) is one of the most saleable cereal crops grown worldwide and a dominant staple food in many developing countries. The severe outbreak of fall armyworm in maize causes massive yield loss. Modern technologies, including smartphones, can assist in detecting recognising the fall armyworm infestation in maize. The objective of this study was to develop an automated Artificial Intelligence Powered Expert System (AIPES) for identifying fall armyworm infestation in maize. In addition, it put forward a deep learning-based model that is trained on photographs of healthy and fall armyworm infested leaves, cobs and tassels from a dataset and furnished an application that will be detecting maize fall armyworm infestation using Convolutional Neural Network (CNN) architecture and Mobile Net V 2 framework model. The study developed an Artificial Intelligence (AI) based maize fall armyworm infestation detection system using a DCNN (Deep Convolutional Neural Network) to support maize cultivating farmers. The model executed the objective by accurately identifying the fall armyworm infested maize plant and also classified them vis-c-vis the healthier crop. The deep learning models were trained to detect and recognise fall armyworm infection using more than 11000 images of fall armyworm infested leaves, cobs, and tassels. The created application (AIPES for identifying fall armyworm infestation in maize) using CNN detected and recognised the fall armyworm infestation in maize with a 100 per cent training accuracy rate and 87 per cent validation accuracy. So, the detection of maize fall armyworm and the treatment of fall armyworm-infested maize could lead to a higher maize crop yield.      

Persistence of foliar applied and pre-storage seed-treated insecticides in rice and its processed products

Abstract A field study was conducted to investigate the persistence of foliar-applied thiamethoxam 25% WG at a rate of 25 g ai ha−1 and chlorantraniliprole 18.5% SC at 30 g ai ha−1 in various parts of rice plants, including whole grain rice, brown rice, bran, husk, straw, and cooked rice. Liquid Chromatography-Mass spectrometry/Mass spectrometry was used for sample analysis. Chlorantraniliprole residues were found to persist in whole grains, bran, husk, and straw at the time of harvest, while thiamethoxam residue was not detected in harvested grains, processed products, or straw. The study concluded that foliar-applied chlorantraniliprole and thiamethoxam did not pose any dietary risk in cooked rice. In a pre-storage seed treatment study, thiamethoxam 30% FS at 3 mL kg−1 was evaluated against Angoumois grain moth infestation during storage. The seeds remained unharmed for nine months and exhibited significantly less moth damage (2.0%) even after twelve months of storage. Thiamethoxam residues persisted for more than one year in whole rice grain, brown rice, bran, and husk with seed treatment, with higher residue levels observed in bran and husk. Parboiling and cooking led to the degradation of thiamethoxam residues

SARNET hydrogen deflagration benchmarks Main outcomes and conclusions

International audienceIn case of a core melt-down accident in a light water nuclear reactor, hydrogen is produced during reactor core degradation and released into the reactor building. This subsequently creates a combustion hazard. A local ignition of the combustible mixture may generate standing flames or initially slow propagating flames. Depending on geometry, mixture composition and turbulence level, the flame can accelerate or be quenched after a certain distance. The loads generated by the combustion process (increase of the containment atmosphere pressure and temperature) may threaten the integrity of the containment building and of internal walls and equipment. Turbulent deflagration flames may generate high pressure pulses, temperature peaks, shock waves and large pressure gradients which could severely damage specific containment components, internal walls and/or safety equipment. The evaluation of such loads requires validated codes which can be used with a high level of confidence. Currently, turbulence and steam effect on flame acceleration, flame deceleration and flame quenching mechanisms are not well reproduced by combustion models usually implemented in safety tools and further model enhancement and validation are still needed. For this purpose, two hydrogen deflagration benchmark exercises have been organised in the framework of the SARNET network. The first benchmark was focused on turbulence effect on flame propagation. For this purpose, three tests performed in the ENACCEF facility were considered. They concern vertical flame propagation in an initially homogenous mixture with 13 vol.% hydrogen content and different geometrical configurations. Three blockage ratios of 0, 0.33 and 0.6 were considered to generate different levels of turbulence. The second benchmark objective was the investigation of the diluting effect on flame propagation. Thus, three tests performed in the ENACCEF facility using the same blockage ratio of 0.63 and three different initial gas compositions (with 10, 20 and 30 vol.% diluents) have been considered. Since ENACCEF runs at ambient temperature, a surrogate to steam was used consisting of a mixture of 0.6He + 0.4CO2 on molar basis. This paper aims to present the benchmarks conclusions regarding the ability of LP and CFD combustion models to predict the effect of turbulence and diluent on flame propagation. © 2014 Elsevier Ltd